Video compression can be considered the process of representing digital video data in a form that uses fewer bits when stored or transmitted. Video compression algorithms can achieve compression by exploiting redundancies and irrelevancies in the video data, whether spatial, temporal, or color-space. Video compression algorithms typically segment the video data into portions, such as groups of frames and groups of pels, to identify areas of redundancy within the video that can be represented with fewer bits than the original video data. When these redundancies in the data are reduced, greater compression can be achieved. An encoder can be used to transform the video data into an encoded format, while a decoder can be used to transform encoded video back into a form comparable to the original video data. The implementation of the encoder/decoder is referred to as a codec.
Standard encoders divide a given video frame into non-overlapping coding units or macroblocks (rectangular regions of contiguous pels) for encoding. The macroblocks are typically processed in a traversal order of left to right and top to bottom in the frame. Compression can be achieved when macroblocks are predicted and encoded using previously-coded data. The process of encoding macroblocks using spatially neighboring samples of previously-coded macroblocks within the same frame is referred to as intra-prediction. Intra-prediction attempts to exploit spatial redundancies in the data. The encoding of macroblocks using similar regions from previously-coded frames, together with a motion estimation model, is referred to as inter-prediction. Inter-prediction attempts to exploit temporal redundancies in the data.
The encoder may generate a residual by measuring the difference between the data to be encoded and the prediction. The residual can provide the difference between a predicted macroblock and the original macroblock. The encoder can generate motion vector information that specifies, for example, the location of a macroblock in a reference frame relative to a macroblock that is being encoded or decoded. The predictions, motion vectors (for inter-prediction), residuals, and related data can be combined with other processes such as a spatial transform, a quantizer, an entropy encoder, and a loop filter to create an efficient encoding of the video data. The residual that has been quantized and transformed can be processed and added back to the prediction, assembled into a decoded frame, and stored in a framestore. Details of such encoding techniques for video will be familiar to a person skilled in the art.
H.264/MPEG-4 Part 10 AVC (advanced video coding), hereafter referred to as H.264, is a codec standard for video compression that utilizes block-based motion estimation and compensation and achieves high quality video representation at relatively low bitrates. This standard is one of the encoding options used for Blu-ray disc creation and within major video distribution channels, including video streaming on the internet, video conferencing, cable television, and direct-broadcast satellite television. The basic coding units for H.264 are 16×16 macroblocks. H.264 is the most recent widely-accepted standard in video compression.
The basic MPEG standard defines three types of frames (or pictures), based on how the macroblocks in the frame are encoded. An I-frame (intra-coded picture) is encoded using only data present in the frame itself. Generally, when the encoder receives video signal data, the encoder creates I frames first and segments the video frame data into macroblocks that are each encoded using intra-prediction. Thus, an I-frame consists of only intra-predicted macroblocks (or “intra macroblocks”). I-frames can be costly to encode, as the encoding is done without the benefit of information from previously-decoded frames. A P-frame (predicted picture) is encoded via forward prediction, using data from previously-decoded I-frames or P-frames, also known as reference frames. P-frames can contain either intra macroblocks or (forward-) predicted macroblocks. A B-frame (bi-predictive picture) is encoded via bidirectional prediction, using data from both previous and subsequent frames. B-frames can contain intra, (forward-)predicted, or bi-predicted macroblocks.
As noted above, conventional inter-prediction is based on block-based motion estimation and compensation (BBMEC). The BBMEC process searches for the best match between the target macroblock (the current macroblock being encoded) and similar-sized regions within previously-decoded reference frames. When a best match is found, the encoder may transmit a motion vector. The motion vector may include a pointer to the best match's frame position as well as information regarding the difference between the best match and the corresponding target macroblock. One could conceivably perform exhaustive searches in this manner throughout the video “datacube” (height×width×frame) to find the best possible matches for each macroblock, but exhaustive search is usually computationally prohibitive. As a result, the BBMEC search process is limited, both temporally in terms of reference frames searched and spatially in terms of neighboring regions searched. This means that “best possible” matches are not always found, especially with rapidly changing data.
A particular set of reference frames is termed a Group of Pictures (GOP). The GOP contains only the decoded pels within each reference frame and does not include information as to how the macroblocks or frames themselves were originally encoded (I-frame, B-frame or P-frame). Older video compression standards, such as MPEG-2, used one reference frame (the previous frame) to predict P-frames and two reference frames (one past, one future) to predict B-frames. The H.264 standard, by contrast, allows the use of multiple reference frames for P-frame and B-frame prediction. While the reference frames are typically temporally adjacent to the current frame, there is also accommodation for the specification of reference frames from outside the set of temporally adjacent frames.
Conventional compression allows for the blending of multiple matches from multiple frames to predict regions of the current frame. The blending is often linear, or a log-scaled linear combination of the matches. One example of when this bi-prediction method is effective is when there is a fade from one image to another over time. The process of fading is a linear blending of two images, and the process can sometimes be effectively modeled using bi-prediction. Some past standard encoders such as the MPEG-2 interpolative mode allow for the interpolation of linear parameters to synthesize the bi-prediction model over many frames.
The H.264 standard also introduces additional encoding flexibility by dividing frames into spatially distinct regions of one or more contiguous macroblocks called slices. Each slice in a frame is encoded (and can thus be decoded) independently from other slices. I-slices, P-slices, and B-slices are then defined in a manner analogous to the frame types described above, and a frame can consist of multiple slice types. Additionally, there is typically flexibility in how the encoder orders the processed slices, so a decoder can process slices in an arbitrary order as they arrive to the decoder.
Historically, model-based compression schemes have been proposed to avoid the limitations of BBMEC prediction. These model-based compression schemes (the most well-known of which is perhaps the MPEG-4 Part 7 standard) rely on the detection and tracking of objects or features in the video and a method for encoding those features/objects separately from the rest of the video frame. These model-based compression schemes, however, suffer from the challenge of segmenting video frames into object vs. non-object (feature vs. non-feature) regions. First, because objects can be of arbitrary size, their shapes need to be encoded in addition to their texture (color content). Second, the tracking of multiple moving objects can be difficult, and inaccurate tracking causes incorrect segmentation, usually resulting in poor compression performance. A third challenge is that not all videos are composed of objects or features, so there needs to be a fallback encoding scheme when objects/features are not present.
While the H.264 standard allows a codec to provide better quality video at lower file sizes than previous standards, such as MPEG-2 and MPEG-4 ASP (advanced simple profile), “conventional” compression codecs implementing the H.264 standard typically have struggled to keep up with the demand for greater video quality and resolution on memory-constrained devices, such as smartphones and other mobile devices, operating on limited-bandwidth networks. Video quality and resolution are often compromised to achieve adequate playback on these devices. Further, as video resolution increases, file sizes increase, making storage of videos on and off these devices a potential concern.